Heuristic functions optimized with respect to \(\mathrm {L}^*\)  loss function are compared to those optimized with respect to \(\mathrm {L}_2\)  loss on two domains: Sokoban and Mazes with teleports. This is supplemented by the comparison with domain-independent planners: (1) SymBA* [1]}, a cost-optimal planner from International Planning Competition (IPC) 2014; (2) Delfi [2]}; (3) Mercury14 [3]}, a satisfycing planner from IPC 2014; (4) Stone soupe [4]}, and by solutions based on Monte Carlo Tree Search [5]} and reinforcement learning [6]}, [7]}. Since authors of [8]} admit that their solution does not work on Sokoban problems, and [9]} works only on small Sokoban problems with two boxes, we do not compare to these works. All experiments involving neural networks have been repeated three times.
